{smcl}
{* 13sep2013}{...}
{hi:help estadd}{right: ({browse "http://www.stata-journal.com/article.html?article=up0043":SJ14-2: st0085_2})}
{hline}

{title:Title}

{p2colset 5 15 17 2}{...}
{p2col :{hi:estadd} {hline 2}}Add results to (stored) estimates{p_end}
{p2colreset}{...}


{title:Syntax}

{p 8 15 2}
{cmd:estadd} {it:{help estadd##subcommand:subcommand}} [{cmd:,}
{it:{help estadd##opts:options}}] [{cmd::} {it:namelist}]


    where {it:namelist} is {cmd:_all} | {cmd:*} | {it:name} [{it:name} ...]

{marker subcommand}{...}
    {it:subcommand}{col 26}Description
    {hline 65}
    Elementary
      {helpb estadd##local:{ul:loc}al} {it:name ...}{col 26}{...}
add a macro
      {helpb estadd##scalar:{ul:sca}lar} {it:name} {cmd:=} {it:exp}{col 26}{...}
add a scalar
      {helpb estadd##matrix:{ul:mat}rix} {it:name} {cmd:=} {it:mat}{col 26}{...}
add a matrix
      {helpb estadd##rreturn:r({it:name})}{col 26}{...}
add contents of {cmd:r(}{it:name}{cmd:)} (matrix or scalar)

    Statistics for each
    coefficient
      {helpb estadd##beta:beta}{col 26}{...}
standardized coefficients
      {helpb estadd##vif:vif}{col 26}{...}
variance inflation factors (after {cmd:regress})
      {helpb estadd##pcorr:pcorr}{col 26}{...}
partial (and semipartial) correlations
      {helpb estadd##expb:expb}{col 26}{...}
exponentiated coefficients
      {helpb estadd##ebsd:ebsd}{col 26}{...}
standardized factor change coefficients
      {helpb estadd##mean:mean}{col 26}{...}
means of regressors
      {helpb estadd##sd:sd}{col 26}{...}
standard deviations of regressors
      {helpb estadd##summ:summ}{col 26}{...}
various descriptives of the regressors

    Summary statistics
      {helpb estadd##coxsnell:coxsnell}{col 26}{...}
Cox and Snell's pseudo-R-squared
      {helpb estadd##nagelkerke:nagelkerke}{col 26}{...}
Nagelkerke's pseudo-R-squared
      {helpb estadd##lrtest:lrtest} {it:model}{col 26}{...}
likelihood-ratio test
      {helpb estadd##ysumm:ysumm}{col 26}{...}
descriptives of the dependent variable

    Other
      {helpb estadd##margins:margins}{col 26}{...}
add results from {cmd:margins} (Stata 11 or later)

    {help estadd##spost:SPost}
      {helpb estadd##brant:brant}{col 26}{...}
add results from {cmd:brant} (if installed)
      {helpb estadd##fitstat:fitstat}{col 26}{...}
add results from {cmd:fitstat} (if installed)
      {helpb estadd##listcoef:listcoef}{col 26}{...}
add results from {cmd:listcoef} (if installed)
      {helpb estadd##mlogtest:mlogtest}{col 26}{...}
add results from {cmd:mlogtest} (if installed)
      {helpb estadd##prchange:prchange}{col 26}{...}
add results from {cmd:prchange} (if installed)
      {helpb estadd##prvalue:prvalue}{col 26}{...}
add results from {cmd:prvalue} (if installed)
      {helpb estadd##asprvalue:asprvalue}{col 26}{...}
add results from {cmd:asprvalue} (if installed)
    {hline 65}

{marker opts}{...}
{synoptset 28}{...}
{synopthdr:options}
{synoptline}
{synopt :{cmdab:r:eplace}}permit overwriting existing {cmd:e()}'s{p_end}
{synopt :{cmdab:p:refix(}{it:string}{cmd:)}}specify prefix for names of added
results{p_end}
{synopt :{cmdab:q:uietly}}suppress output from subcommand (if any){p_end}
{synopt :{it:subcmdopts}}subcommand specific options{p_end}
{synoptline}


{title:Description}

{p 4 4 2}
{cmd:estadd} adds additional results to the {cmd:e()}-returns of an
estimation command (see {help estcom}, {helpb ereturn}).  If no
{it:namelist} is provided, then the results are added to the
currently active estimates (that is, the model fit last).  If these
estimates have been previously stored, the stored copy of the
estimates will also be modified.  Alternatively, if {it:namelist} is
provided after the colon, results are added to all indicated sets of
stored estimates (see {helpb estimates store} or 
{helpb eststo}).  You may use the {cmd:*} and {cmd:?}
wildcards in {it:namelist}.  Execution is silent if {it:namelist} is
provided.

{p 4 4 2}
Adding additional results to the {cmd:e()}-returns is useful, for example,
if the estimates are tabulated by commands such as {helpb estout}
or {helpb esttab}.  See {it:{help estadd##examples:Examples}} below for
illustration of the usage of {cmd:estadd}.

{p 4 4 2}
Technical note: Some of the subcommands below use the
information contained in {cmd:e(sample)} to determine estimation sample.
These subcommands return an error if the estimates do not contain
{cmd:e(sample)}.


{title:Subcommands}

{dlgtab:Elementary}

{marker local}{...}
{p 4 8 2}
{cmd:estadd} {cmdab:loc:al} {it:name ...}

{p 8 8 2}
adds in macro {cmd:e(}{it:name}{cmd:)} the specified contents (also
see {helpb ereturn}).

{marker scalar}{...}
{p 4 8 2}
{cmd:estadd} {cmdab:sca:lar} {it:name} {cmd:=} {it:exp}

{p 8 8 2}
adds in scalar {cmd:e(}{it:name}{cmd:)} the evaluation of {it:exp}
(also see {helpb ereturn}).

{p 4 8 2}
{cmd:estadd} {cmdab:sca:lar} {cmd:r(}{it:name}{cmd:)}

{p 8 8 2}
adds in scalar {cmd:e(}{it:name}{cmd:)} the value of scalar {cmd:r(}{it:name}{cmd:)}.

{p 4 8 2}
{cmd:estadd} {cmdab:sca:lar} {it:name}

{p 8 8 2}
adds in scalar {cmd:e(}{it:name}{cmd:)} the the value of scalar {it:name}.

{marker matrix}{...}
{p 4 8 2}
{cmd:estadd} {cmdab:mat:rix} {it:name} {cmd:=} {it:matrix_expression}

{p 8 8 2}
adds in matrix {cmd:e(}{it:name}{cmd:)} the evaluation of {it:matrix_expression}
(also see {helpb matrix define}).

{p 4 8 2}
{cmd:estadd} {cmdab:mat:rix} {cmd:r(}{it:name}{cmd:)}

{p 8 8 2}
adds in matrix {cmd:e(}{it:name}{cmd:)} a copy of matrix {cmd:r(}{it:name}{cmd:)}.

{p 4 8 2}
{cmd:estadd} {cmdab:mat:rix} {it:name}

{p 8 8 2}
adds in matrix {cmd:e(}{it:name}{cmd:)} a copy of matrix {it:name}.

{marker rreturn}{...}
{p 4 8 2}
{cmd:estadd} {cmd:r(}{it:name}{cmd:)}

{p 8 8 2}
adds in {cmd:e(}{it:name}{cmd:)} the value of scalar {cmd:r(}{it:name}{cmd:)}
or a copy of matrix {cmd:r(}{it:name}{cmd:)}, depending on the nature of
{cmd:r(}{it:name}{cmd:)}.


{dlgtab:Statistics for each coefficient}

{marker beta}{...}
{p 4 8 2}
{cmd:estadd} {cmd:beta}

{p 8 8 2}
adds in {cmd:e(beta)} the standardized beta coefficients.

{marker vif}{...}
{p 4 8 2}
{cmd:estadd} {cmd:vif} [{cmd:,} {cmdab:tol:erance} {cmdab:sqr:vif}]

{p 8 8 2}
adds in {cmd:e(vif)} the variance inflation factors (VIFs) for the
regressors (see {helpb vif}).  Note that {cmd:vif} only works
with estimates produced by {helpb regress}.  Additionally, {cmd:tolerance}
adds the tolerances (1/VIF) in {cmd:e(tolerance)}, and
{cmd:sqrvif} adds the square roots of the VIFs in
{cmd:e(sqrvif)}.

{marker pcorr}{...}
{p 4 8 2}
{cmd:estadd} {cmd:pcorr} [{cmd:, semi}]

{p 8 8 2}
adds in {cmd:e(pcorr)} the partial correlations (see {helpb pcorr}) and,
optionally, adds in {cmd:e(spcorr)} the semipartial correlations between the dependent
variable and the individual regressors if {cmd:semi} is specified (see, for example, the {cmd:pcorr2}
package from the SSC archive).  In the case of multiple-equations
models, the results are computed for only the first equation.

{marker expb}{...}
{p 4 8 2}
{cmd:estadd} {cmd:expb} [{cmd:,} {cmdab:nocons:tant}]

{p 8 8 2}
adds in {cmd:e(expb)} the exponentiated coefficients (see the 
{it:{help eform_option}}).  {cmd:noconstant} excludes the constant
from the added results.

{marker ebsd}{...}
{p 4 8 2}
{cmd:estadd} {cmd:ebsd}

{p 8 8 2}
adds in {cmd:e(ebsd)} the standardized factor change coefficients that are
sometimes reported for logistic regression, that is, exp(b_jS_j), where b_j is
the raw coefficient and S_j is the standard deviation of regressor j (see Long
1997).

{marker mean}{...}
{p 4 8 2}
{cmd:estadd} {cmd:mean}

{p 8 8 2}
adds in {cmd:e(mean)} the means of the regressors.

{marker sd}{...}
{p 4 8 2}
{cmd:estadd} {cmd:sd} [{cmd:,} {cmdab:nob:inary}]

{p 8 8 2}
adds in {cmd:e(sd)} the standard deviations of the regressors.
{cmd:nobinary} suppresses the computation of the standard deviation
for 0/1 variables.

{marker summ}{...}
{p 4 8 2}
{cmd:estadd} {cmd:summ} [{cmd:,} {it:stats}]

{p 8 8 2}
adds vectors of the regressors' descriptive statistics to the
estimates.  The following {it:stats} are available:

{marker stats}{...}
        {it:stats}{col 26}Description
        {hline 59}
          {cmdab:me:an}{col 26}mean
          {cmdab:su:m}{col 26}sum
          {cmdab:mi:n}{col 26}minimum
          {cmdab:ma:x}{col 26}maximum
          {cmdab:ra:nge}{col 26}range = max - min
          {cmd:sd}{col 26}standard deviation
          {cmdab:v:ar}{col 26}variance
          {cmd:cv}{col 26}coefficient of variation (sd/mean)
          {cmdab:sem:ean}{col 26}standard error of mean = sd/sqrt(n)
          {cmdab:sk:ewness}{col 26}skewness
          {cmdab:k:urtosis}{col 26}kurtosis
          {cmd:p1}{col 26}1st percentile
          {cmd:p5}{col 26}5th percentile
          {cmd:p10}{col 26}10th percentile
          {cmd:p25}{col 26}25th percentile
          {cmd:p50}{col 26}50th percentile
          {cmd:p75}{col 26}75th percentile
          {cmd:p90}{col 26}90th percentile
          {cmd:p95}{col 26}95th percentile
          {cmd:p99}{col 26}99th percentile
          {cmd:iqr}{col 26}interquartile range = p75 - p25
          {cmd:all}{col 26}all the above
          {cmdab:med:ian}{col 26}equivalent to specifying "{cmd:p50}"
          {cmd:q}{col 26}equivalent to specifying "{cmd:p25 p50 p75}"
        {hline 59}

{p 8 8 2}
The default is {cmd:mean sd min max}.  Alternatively, indicate the
desired statistics.  For example, to add information on the
regressors' skewness and kurtosis, type

            {inp:. estadd summ, skewness kurtosis}

{p 8 8 2}
The statistics' names are used as the names for the returned {cmd:e()}
matrices.  For example, {cmd:estadd summ, mean} will store the means
of the regressors in {cmd:e(mean)}.

{dlgtab:Summary statistics}

{marker coxsnell}{...}
{p 4 8 2}
{cmd:estadd} {cmd:coxsnell}

{p 8 8 2}
adds in {cmd:e(coxsnell)} the Cox and Snell pseudo-R-squared, which is
defined as

{p 12 12 2}
r2_coxsnell = 1 - ( L0 / L1 )^(2/N)

{p 8 8 2}
where L0 is the likelihood of the model without regressors, L1 the
likelihood of the full model, and N is the sample size.

{marker nagelkerke}{...}
{p 4 8 2}
{cmd:estadd} {cmd:nagelkerke}

{p 8 8 2}
adds in {cmd:e(nagelkerke)} the Nagelkerke pseudo R-squared (or Cragg
& Uhler pseudo R-squared), which is defined as

{p 12 12 2}
r2_nagelkerke = r2_coxsnell / (1 - L0^(2/N))

{marker lrtest}{...}
{p 4 8 2}
{cmd:estadd} {cmd:lrtest} {it:model} [{cmd:,} {cmdab:n:ame:(}{it:string}{cmd:)}
{it:{help lrtest:lrtest_options}}]

{p 8 8 2}
adds in {cmd:e(lrtest_chi2)}, {cmd:e(lrtest_df)}, and {cmd:e(lrtest_p)} the
results from a likelihood-ratio test, where {it:model} is the comparison model
(see {helpb lrtest}).  The names may be modified using the {cmd:name()}
option. Specify {cmd:name(}{it:myname}{cmd:)} to add
{cmd:e(}{it:myname}{cmd:chi2)}, {cmd:e(}{it:myname}{cmd:df)}, and
{cmd:e(}{it:myname}{cmd:p)}.  See {helpb lrtest} for the {it:lrtest_options}.

{marker ysumm}{...}
{p 4 8 2}
{cmd:estadd} {cmd:ysumm} [{cmd:,} {it:stats}]

{p 8 8 2}
adds descriptive statistics of the dependent variable.  See the
{helpb estadd##summ:summ} subcommand above for a list of the available
{it:stats}.  The default is {cmd:mean sd min max}.  The default prefix
for the names of the added scalars is {cmd:y} (for example, the mean of the
dependent variable will be returned in {cmd:e(ymean)}).  Use
{cmd:estadd}'s {cmd:prefix()} option to change the prefix.  If a model
has multiple dependent variables, results for the first variable will
be added.

{dlgtab:Other}
{marker margins}{...}

{p 4 8 2}
{cmd:estadd} {cmd:margins} [{it:marginlist}] {ifin} [{it:weight}]
[{cmd:,} {it:options}]

{p 8 8 2} 
adds results from the {cmd:margins} command, which was introduced 
in Stata 11.  See {helpb margins} for options.  All results returned by 
{cmd:margins} except {cmd:e(V)} are added using "{cmd:margins_}" as a default 
prefix.  For example, the margins are added in {cmd:e(margins_b)}.  The 
standard errors are added in {cmd:e(margins_se)}.  Use the {helpb estadd##opts:prefix()} 
option to change the default prefix.

{marker spost}{...}
{dlgtab:SPost}

{p 4 4 2} The following subcommands are wrappers for
commands from Long and Freese's {helpb SPost} package (see
{browse "http://www.indiana.edu/~jslsoc/spost.htm":http://www.indiana.edu/~jslsoc/spost.htm}).  Type

        {bf:. {net "from http://www.indiana.edu/~jslsoc/stata":net from http://www.indiana.edu/~jslsoc/stata}}

{p 4 4 2}
to obtain the latest {cmd:SPost} version (spost9_ado).  {cmd:SPost} for Stata 8 (spostado) is not
supported.

{p 4 4 2}For examples on using the subcommands, see
{browse "http://repec.org/bocode/e/estout/spost.html":http://repec.org/bocode/e/estout/spost.html}.

{marker brant}{...}
{p 4 8 2}
{cmd:estadd brant} [{cmd:,} {it:{help brant:brant_options}}]

{p 8 8 2}
applies {helpb brant} from Long and
Freese's {helpb SPost} package and adds the returned results to
{cmd:e()}.  You may specify {it:brant_options} as described in
{helpb brant}.  The following results are added:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
        Scalars
          {cmd:brant_chi2}  chi-squared of overall Brant test
          {cmd:brant_df}    degrees of freedom of overall Brant test
          {cmd:brant_p}     p-value of overall Brant test

        Matrices
          {cmd:brant}       test results for individual regressors
                      (rows: chi2, p<chi2)
        {hline 60}

{p 4 4 2}
To address the rows of {cmd:e(brant)} in {helpb estout}'s {cmd:cells()} 
option, type {cmd:brant[chi2]} and {cmd:brant[p<chi2]}.

{marker fitstat}{...}
{p 4 8 2}
{cmd:estadd fitstat} [{cmd:,} {it:{help fitstat:fitstat_options}}]

{p 8 8 2}
applies {helpb fitstat} from Long and
Freese's {helpb SPost} package and adds the returned scalars to
{cmd:e()}.  You may specify {it:fitstat_options} as described in
{helpb fitstat}.  Depending on model
and options, a selection of the following scalar statistics is added:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
	Scalars
          {cmd:dev}           deviance (D)
          {cmd:dev_df}        degrees of freedom of D
          {cmd:lrx2}          LR or Wald X2
          {cmd:lrx2_df}       degrees of freedom of X2
          {cmd:lrx2_p}        prob > LR or Wald X2
          {cmd:r2_adj}        adjusted R2
          {cmd:r2_mf}         McFadden's R2
          {cmd:r2_mfadj}      McFadden's Adj R2
          {cmd:r2_ml}         ML (Cox-Snell) R2
          {cmd:r2_cu}         Cragg-Uhler(Nagelkerke) R2
          {cmd:r2_mz}         McKelvey & Zavoina's R2
          {cmd:r2_ef}         Efron's R2
          {cmd:v_ystar}       variance of y*
          {cmd:v_error}       variance of error
          {cmd:r2_ct}         count R2
          {cmd:r2_ctadj}      adjusted count R2
          {cmd:aic0}          AIC
          {cmd:aic_n}         AIC*n
          {cmd:bic0}          BIC
          {cmd:bic_p}         BIC'
          {cmd:statabic}      BIC used by Stata
          {cmd:stataaic}      AIC used by Stata
          {cmd:n_rhs}         number of rhs variables
          {cmd:n_parm}        number of parameters
        {hline 60}

{marker listcoef}{...}
{p 4 8 2}
{cmd:estadd listcoef} [{it:varlist}] [{cmd:,} {cmd:nosd} {it:{help listcoef:listcoef_options}}]

{p 8 8 2}
applies {helpb listcoef} from Long and
Freese's {helpb SPost} package and adds the returned results to
{cmd:e()}.  You may specify {it:listcoef_options} as described in
{helpb listcoef}.  Furthermore,  option {cmd:nosd} suppresses 
adding the standard deviations of the variables in {cmd:e(b_sdx)}.

{p 8 8 2}Depending on the estimation command and options, several of the
following matrices are added:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
	Matrices
          {cmd:b_xs}          x-standardized coefficients
          {cmd:b_ys}          y-standardized coefficients
          {cmd:b_std}         fully standardized coefficients
          {cmd:b_fact}        factor change coefficients
          {cmd:b_facts}       standardized factor change coefficients
          {cmd:b_pct}         percent change coefficients
          {cmd:b_pcts}        standardized percent change coefficients
          {cmd:b_sdx}         standard deviation of the Xs
        {hline 60}

{p 8 8 2}
For nominal models ({helpb mlogit}, {helpb mprobit}), the original
parametrization of {cmd:e(b)} may not match the contrasts computed by
{cmd:listcoef}.  To be able to tabulate standardized coefficients with the raw
coefficients for the requested contrasts, the following matrices are added for
these models:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
	Matrices
          {cmd:b_raw}         raw coefficients
          {cmd:b_se}          standard errors of raw coefficients
          {cmd:b_z}           z statistics
          {cmd:b_p}           p-values
        {hline 60}

{marker mlogtest}{...}
{p 4 8 2}
{cmd:estadd mlogtest} [{it:varlist}] [{cmd:,} {it:{help mlogtest:mlogtest_options}}]

{p 8 8 2}
applies {helpb mlogtest} from Long and
Freese's {helpb SPost} package and adds the returned results to
{cmd:e()}.  You may specify {it:mlogtest_options} as described in
{helpb mlogtest}.

{p 8 8 2}Depending on the specified options, a selection of the following
returns are added:

        {cmd:e(}{it:...}{cmd:)}               Contents
        {hline 60}
        Scalars
          {cmd:hausman_set}{it:#}{cmd:_chi2}  Hausman IIA tests using {helpb hausman}
          {cmd:hausman_set}{it:#}{cmd:_df}
          {cmd:hausman_set}{it:#}{cmd:_p}

          {cmd:suest_set}{it:#}{cmd:_chi2}    Hausman IIA tests using {helpb suest}
          {cmd:suest_set}{it:#}{cmd:_df}
          {cmd:suest_set}{it:#}{cmd:_p}

          {cmd:smhsiao_set}{it:#}{cmd:_chi2}  Small-Hsiao IIA tests
          {cmd:smhsiao_set}{it:#}{cmd:_df}
          {cmd:smhsiao_set}{it:#}{cmd:_p}

          {cmd:combine_}{it:#1}{cmd:_}{it:#2}{cmd:_chi2} Wald tests for combination of outcomes
          {cmd:combine_}{it:#1}{cmd:_}{it:#2}{cmd:_df}
          {cmd:combine_}{it:#1}{cmd:_}{it:#2}{cmd:_p}

          {cmd:lrcomb_}{it:#1}{cmd:_}{it:#2}{cmd:_chi2}  LR tests for combination of outcomes
          {cmd:lrcomb_}{it:#1}{cmd:_}{it:#2}{cmd:_df}
          {cmd:lrcomb_}{it:#1}{cmd:_}{it:#2}{cmd:_p}

          {cmd:wald_set}{it:#}{cmd:_chi2}     Wald tests for sets of independent
          {cmd:wald_set}{it:#}{cmd:_df}       variables
          {cmd:wald_set}{it:#}{cmd:_p}

          {cmd:lrtest_set}{it:#}{cmd:_chi2}   LR tests for sets of independent
          {cmd:lrtest_set}{it:#}{cmd:_df}     variables
          {cmd:lrtest_set}{it:#}{cmd:_p}

        Matrices
          {cmd:wald}               Wald tests for individual variables
                             (rows: chi2, df, p)
          {cmd:lrtest}             LR tests for individual variables
                             (rows: chi2, df, p)
        {hline 60}

{p 4 4 2}
To address the rows of {cmd:e(wald)} and {cmd:e(lrtest)} in {helpb estout}'s 
{cmd:cells()} option, type the row names in brackets, for example, {cmd:wald[p]} or 
{cmd:lrtest[chi2]}.

{marker prchange}{...}
{p 4 8 2}
{cmd:estadd prchange} [{it:varlist}] {ifin} [{cmd:,}
 {cmdab:pa:ttern(}{it:typepattern}{cmd:)} {cmdab:b:inary(}{it:type}{cmd:)} {cmdab:c:ontinuous(}{it:type}{cmd:)}
 [{cmd:no}]{cmdab:a:vg} {cmd:split}[{cmd:(}{it:prefix}{cmd:)}] {it:{help prchange:prchange_options}}]

{p 8 8 2}
applies {helpb prchange} from Long and
Freese's {helpb SPost} package and adds the returned results to
{cmd:e()}.  You can specify {it:prchange_options} as described in
{helpb prchange}.  In particular, the {cmd:outcome()} option can be
used with models for count, ordered, or nominal outcomes
to request results for a specific outcome.  Further options are

{p 8 12 2}
{cmd:pattern(}{it:typepattern}{cmd:)}, {cmd:binary(}{it:type}{cmd:)}, and
{cmd:continuous(}{it:type}{cmd:)} determine which types of discrete change
effects are added as the main results.  The default is to add the 0 to 1
change effect for binary variables and the standard deviation change effect
for continuous variables. Use {cmd:binary(}{it:type}{cmd:)} and
{cmd:continuous(}{it:type}{cmd:)} to change these defaults.  Available
types are

                {it:type}        Description
                {hline 48}
                {cmdab:mi:nmax}      minimum to maximum change effect
                {cmdab:0:1}          0 to 1 change effect
                {cmdab:d:elta}       {cmd:delta()} change effect
                {cmdab:s:d}          standard deviation change effect
                {cmdab:m:argefct}    marginal effect (some models only)
                {hline 48}

{p 12 12 2}
Use {cmd:pattern(}{it:typepattern}{cmd:)} if you want to determine the type of
the added effects individually for each regressor.  For example,
{bind:{cmd:pattern(minmax sd delta)}} would add {cmd:minmax} for the first
regressor, {cmd:sd} for the second, and {cmd:delta} for the third, and then
proceed using the defaults for the remaining variables.

{p 8 12 2}
{cmd:avg} requests that only the average results over all outcomes are added
if applied to ordered or nominal models ({helpb ologit}, {helpb oprobit},
{helpb slogit}, {helpb mlogit}, {helpb mprobit}).  The default is to add the
average results as well as the individual results for the different outcomes
(unless {helpb prchange}'s {cmd:outcome()} option is specified, in which case
only results for the indicated outcome are added).  Furthermore, specify
{cmd:noavg} to suppress the average results and add only the outcome-specific
results.  {cmd:avg} cannot be combined with {cmd:split} or {cmd:outcome()}.

{p 8 12 2}{cmd:split}[{cmd:(}{it:prefix}{cmd:)}] saves
each outcome's results in a separate estimation set if applied to ordered
or nominal models ({helpb ologit}, {helpb oprobit}, {helpb slogit}, {helpb mlogit},
{helpb mprobit}).  The estimation sets are named
{it:prefix}{it:#}, where {it:#} is the value of the outcome at hand.  If no
{it:prefix} is provided, the name of the estimation set followed by an
underscore is used as the prefix.  If the estimation set has no name
because it has not been stored yet, the name of the estimation command
followed by an underscore is used as the prefix (for example, {cmd:ologit_}).  The
estimation sets stored by the {cmd:split} option are intended for
tabulation only and should not be used with other postestimation
commands.

{p 8 8 2}Depending on model and options, several of the following matrices
and scalars are added:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
        Scalars
          {cmd:centered}    {cmd:1} if effects are centered, {cmd:0} else
          {cmd:delta}       value of {cmd:delta()}
          {cmd:predval}[{it:#}]  prediction(s) at the base values
          {cmd:outcome}     outcome value ({cmd:outcome()}/{cmd:split} only)

        Matrices
          {cmd:dc}          discrete change effects (rows: main, minmax,
                      01, delta, sd [, margefct])
          {cmd:pattern}     types of effects in the main row of {cmd:e(dc)}
          {cmd:X}           base values and descriptive statistics
                      (rows: X, SD, Min, Max)
        {hline 60}

{p 8 8 2}The {cmd:e(dc)} and {cmd:e(X)} matrices have multiple rows.  The
{cmd:e(dc)} matrix contains the main results as determined by
{cmd:pattern()}, {cmd:binary()}, and {cmd:continuous()} in the first row.
The second and following rows contain the separate results for each type of
effect, using the labels provided by {cmd:prchange} as row names.  Type
{cmd:dc[}{it:#}{cmd:]} or {cmd:dc[}{it:rowname}{cmd:]} to address the rows
in {helpb estout}'s {cmd:cells()} option, where {it:#} is the row number 
or {it:rowname} is the
row name.  For example, type {cmd:dc[-+sd/2]} to address the centered
standard deviation change effects.  To tabulate the main results (1st row),
simply type {cmd:dc}.  {cmd:e(pattern)} indicates the types of effects
contained in the main row of {cmd:e(dc)} using numeric codes.  The codes are 1
for the minimum to maximum change effect, 2 for the 0 to 1 change effect, 3
for the {cmd:delta()} change effect, 4 for the standard deviation change
effect, and 5 for the marginal effect.  {cmd:e(X)} has four rows
containing the base values, standard deviations, minimums, and maximums.  If
the {cmd:fromto} option is specified, two additional matrices,
{cmd:e(dcfrom)} and {cmd:e(dcto)}, are added.

{marker prvalue}{...}
{p 4 8 2}
{cmd:estadd prvalue} {ifin} [{cmd:,} {cmdab:lab:el:(}{it:string}{cmd:)}
{it:{help prvalue:prvalue_options}}]

{p 4 8 2}
{cmd:estadd prvalue} {cmd:post} [{it:name}] [{cmd:,} {cmdab:t:itle:(}{it:string}{cmd:)} {cmd:swap}]

{p 8 8 2} applies {helpb prvalue} from Long and Freese's {helpb SPost}
package and adds the returned results to {cmd:e()}.  The procedure is to
first collect a series of predictions by repeated calls to
{cmd:estadd prvalue} and then apply {cmd:estadd prvalue post} to prepare the results
for tabulation as in the following example:

            {com}. logit lfp k5 k618 age wc hc lwg inc
            . estadd prvalue, x(inc 10) label(low inc)
            . estadd prvalue, x(inc 20) label(med inc)
            . estadd prvalue, x(inc 30) label(high inc)
            . estadd prvalue post
            . estout{txt}

{p 8 8 2} You may specify {it:prvalue_options} with {cmd:estadd prvalue} as
described in {helpb prvalue}.  For example, use {cmd:x()} and
{cmd:rest()} to set the values of the independent variables.  Use
{cmd:label()} to label the single calls.  "pred#" is used as label if
{cmd:label()} is omitted, where # is the number of the call.  Labels may
contain spaces, but they will be trimmed to a maximum
length of 30 characters and some characters ({cmd::},
{cmd:.}, {cmd:"}) will be replaced by an underscore.  The results
from the single calls are collected in matrix {cmd:e(_estadd_prvalue)}
(predictions) and matrix {cmd:e(_estadd_prvalue_x)} (x-values).  Specify
{cmd:replace} to drop results from previous calls.

{p 8 8 2}
{cmd:estadd prvalue post} posts the collected predictions in {cmd:e(b)}
so that they can be tabulated.  The following results are stored:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
        Scalars
          {cmd:N}           number of observations

        Macros
          {cmd:depvar}      name of dependent variable
          {cmd:cmd}         {cmd:estadd_prvalue}
          {cmd:model}       model estimation command
          {cmd:properties}  {cmd:b}

        Matrices
          {cmd:b}           predictions
          {cmd:se}          standard errors
          {cmd:LB}          lower confidence interval bounds
          {cmd:UB}          upper confidence interval bounds
          {cmd:Category}    outcome values
          {cmd:Cond}        conditional predictions (some models only)
          {cmd:X}           values of predictors (for each prediction)
          {cmd:X2}          second equation predictors (some models only)
        {hline 60}

{p 8 8 2} {cmd:estadd prvalue post} replaces the current model unless
{it:name} is specified, in which case the results are stored under {it:name} and the model
remains active.  However, if the model has a name
(because it has been stored), the name of the model is used as a prefix.
If, for example, the model has been stored as {cmd:model1}, then
{cmd:estadd prvalue post} stores its results under {cmd:model1}{it:name}.
Use {cmd:title()} to specify a title for the stored results.

{p 8 8 2}
By default, {cmd:estadd prvalue post} arranges {cmd:e(b)} in a way so that
predictions are grouped by outcome (that is, outcome labels are used as
equations).  Alternatively, you can specify {cmd:swap} to group predictions by
{cmd:prvalue} calls (that is, to use the prediction labels as equations).

{p 8 8 2}
{cmd:e(X)} contains one row for each independent variable.  To address the
rows in {helpb estout}'s {cmd:cells()} option, type
{cmd:X[}{it:varname}{cmd:]}, where {it:varname} is the name of the variable of
interest.  {cmd:e(X2)}, if provided, is analogous to {cmd:e(X)}.

{marker asprvalue}{...}
{p 4 8 2}
{cmd:estadd asprvalue} [{cmd:,} {cmdab:lab:el:(}{it:string}{cmd:)}
{it:{help asprvalue:asprvalue_options}}]

{p 4 8 2}
{cmd:estadd asprvalue} {cmd:post} [{it:name}] [{cmd:,} {cmdab:t:itle:(}{it:string}{cmd:)} {cmd:swap}]

{p 8 8 2} applies {helpb asprvalue} from Long and Freese's {helpb SPost}
package and adds the returned results to {cmd:e()}.  The procedure is to
first collect a series of predictions by repeated calls to
{cmd:estadd asprvalue} and then apply {cmd:estadd asprvalue post} to prepare the results
for tabulation as in the following example:

            {com}. clogit choice train bus time invc, group(id)
            . estadd asprvalue, cat(train bus) label(at means)
            . estadd asprvalue, cat(train bus) rest(asmean) label(at asmeans)
            . estadd asprvalue post
            . estout{txt}

{p 8 8 2}
You may specify {it:asprvalue_options} with {cmd:estadd asprvalue} as
described in {helpb asprvalue}.  For example, use {cmd:x()} and
{cmd:rest()} to set the values of the independent variables.  Use
{cmd:label()} to label the single calls.  "pred#" is used as label if
{cmd:label()} is omitted, where # is the number of the call.  Labels may
contain spaces, but they will be trimmed to a maximum
length of 30 characters and some characters ({cmd::},
{cmd:.}, {cmd:"}) will be replaced by an underscore.  The results
from the single calls are collected in matrices {cmd:e(_estadd_asprval)}
(predictions), {cmd:e(_estadd_asprval_asv)} (values of alternative-specific
variables), and {cmd:e(_estadd_asprval_csv)} (values of case-specific
variables).  Specify {cmd:replace} to drop results from previous calls.

{p 8 8 2}
{cmd:estadd asprvalue post} posts the collected predictions in {cmd:e(b)}
so that they can be tabulated.  The following results are stored:

        {cmd:e(}{it:...}{cmd:)}        Contents
        {hline 60}
        Scalars
          {cmd:N}           number of observations

        Macros
          {cmd:depvar}      name of dependent variable
          {cmd:cmd}         {cmd:estadd_asprvalue}
          {cmd:model}       model estimation command
          {cmd:properties}  {cmd:b}

        Matrices
          {cmd:b}           predictions
          {cmd:asv}         alternative-specific variables (if available)
          {cmd:csv}         case-specific variables (if available)
        {hline 60}

{p 8 8 2}
{cmd:estadd asprvalue post} replaces the current model unless
{it:name} is specified, in which case the results are stored under
{it:name} and the model remains active.  However, if the model has a name
(because it has been stored), the name of the model is used as a prefix.
If, for example, the model has been stored as {cmd:model1}, then
{cmd:estadd asprvalue post} stores its results under {cmd:model1}{it:name}.
Use {cmd:title()} to specify a title for the stored results.

{p 8 8 2}
By default, {cmd:estadd asprvalue post} arranges
{cmd:e(b)} in a way so that predictions are grouped by outcome (that is, outcome labels are used
as equations).  Alternatively, you can specify {cmd:swap} to group predictions by
{cmd:prvalue} calls (that is, to use the prediction labels as equations).

{p 8 8 2}
{cmd:e(asv)} and {cmd:e(csv)} contain one row for each variable.
To address the rows in {helpb estout}'s {cmd:cells()} option, type
{cmd:asv[}{it:varname}{cmd:]} or {cmd:csv[}{it:varname}{cmd:]}, where
{it:varname} is the name of the variable of interest.

{marker options}{...}
{title:Options}

{p 4 8 2}
{cmd:replace} permits {cmd:estadd} to overwrite existing {cmd:e()}
macros, scalars, or matrices.

{p 4 8 2}
{cmd:prefix(}{it:string}{cmd:)} denotes a prefix for the names of the
added results.  The default prefix is an empty string.  For example, if
{cmd:prefix(}{it:string}{cmd:)} is specified, the {cmd:beta}
subcommand will return the matrix {cmd:e(}{it:string}{cmd:beta)}.

{p 4 8 2}
{cmd:quietly} suppresses the output from the called subcommand and displays
only the list of added results.  Note that many of {cmd:estadd}'s subcommands
do not generate output, in which case {cmd:quietly} has no effect.

{p 4 8 2}
{it:subcmdopts} are subcommand-specific options.  See the descriptions
of the subcommands above.


{marker examples}{...}
{title:Examples}

{p 4 4 2}Example 1: Add {cmd:r()}-returns from other programs to the
current estimates

        {com}. sysuse auto
        {txt}(1978 Automobile Data)

        {com}. quietly regress price mpg weight
        {txt}
        {com}. test mpg=weight

        {txt} ( 1)  {res}mpg - weight = 0

        {txt}       F(  1,    71) ={res}    0.36
        {txt}{col 13}Prob > F ={res}    0.5514
        {txt}
        {com}. estadd scalar p_diff = r(p)

        {txt}added scalar:
                     e(p_diff) =  {res}.55138216
        {txt}
        {com}. estout, stats(p_diff)
        {res}
        {txt}{hline 25}
        {txt}                        b
        {txt}{hline 25}
        {txt}mpg         {res}    -49.51222{txt}
        {txt}weight      {res}     1.746559{txt}
        {txt}_cons       {res}     1946.069{txt}
        {txt}{hline 25}
        {txt}p_diff      {res}     .5513822{txt}
        {txt}{hline 25}


{p 4 4 2}
Example 2: Add means and standard deviations of the model's regressors
to the current estimates

        {com}. quietly logit foreign price mpg
        {txt}
        {com}. estadd summ, mean sd

        {txt}added matrices:
                         e(sd) :  {res}1 x 3
                       {txt}e(mean) :  {res}1 x 3
        {txt}
        {com}. estout, cells("mean sd") drop(_cons)
        {res}
        {txt}{hline 38}
        {txt}                     mean           sd
        {txt}{hline 38}
        {txt}price       {res}     6165.257     2949.496{txt}
        {txt}mpg         {res}      21.2973     5.785503{txt}
        {txt}{hline 38}


{p 4 4 2}
Example 3: Add standardized beta coefficients to stored estimates

        {com}. eststo: quietly regress price mpg
        {txt}({res}est1{txt} stored)

        {com}. eststo: quietly regress price mpg foreign
        {txt}({res}est2{txt} stored)

        {com}. estadd beta: *
        {txt}
        {com}. estout, cells(beta)  drop(_cons)
        {res}
        {txt}{hline 38}
        {txt}                     est1         est2
        {txt}                     beta         beta
        {txt}{hline 38}
        {txt}mpg         {res}    -.4685967    -.5770712{txt}
        {txt}foreign     {res}                  .2757378{txt}
        {txt}{hline 38}


{p 4 4 2}See
{browse "http://repec.org/bocode/e/estout":http://repec.org/bocode/e/estout}
for additional examples.


{title:Writing one's own subcommands}

{p 4 4 2}
A program providing a new {cmd:estadd} subcommand should be called
{cmd:estadd_}{it:mysubcommand} (see {helpb program} for advice on defining
programs).  {it:mysubcommand} will be available to {cmd:estadd} as a new
{it:subcommand} after the program definition has been executed or saved to a
file called {cmd:estadd_}{it:mysubcommand}{cmd:.ado} in either the current
directory or somewhere else in the {cmd:adopath} (see {helpb sysdir}).

{p 4 4 2}
Use the subcommands provided within {cmd:estadd.ado} as a starting
point for writing new subcommands.  See
{browse "http://repec.org/bocode/e/estout/estadd.html#estadd007":http://repec.org/bocode/e/estout/estadd.html#estadd007}
for an example.


{title:Author}

{p 4 4 2}Ben Jann, Institute of Sociology, University of Bern, jann@soz.unibe.ch


{title:Also see}

{p 4 14 2}Article:  {it:Stata Journal}, volume 14, number 2: {browse "http://www.stata-journal.com/article.html?article=up0043":st0085_2},{break}
                    {it:Stata Journal}, volume 7, number 2: {browse "http://www.stata-journal.com/article.html?article=up0018":st0085_1},{break}
                    {it:Stata Journal}, volume 5, number 3: {browse "http://www.stata-journal.com/article.html?article=st0085":st0085}{p_end}

{p 5 14 2}Manual:  {manlink R estimates}

{p 7 14 2}Help:
 {helpb estimates},
 {helpb ereturn},
 {helpb program},
 {helpb esttab}, 
 {helpb estout}, 
 {helpb eststo}, 
 {helpb estpost}
{p_end}
